Predicting the Impact of Future Land Use and Climate Change on Potential Soil Erosion Risk in an Urban District of the Harare Metropolitan Province, Zimbabwe

Abstract

Monitoring urban area expansion through multispectral remotely sensed data and other geomatics techniques is fundamental for sustainable urban planning. Forecasting of future land use land cover (LULC) change for the years 2034 and 2050 was performed using the Cellular Automata Markov model for the current fast-growing Epworth district of the Harare Metropolitan Province, Zimbabwe. The stochastic CA–Markov modelling procedure validation yielded kappa statistics above 80%, ascertaining good agreement. The spatial distribution of the LULC classes CBD/Industrial area, water and irrigated croplands as projected for 2034 and 2050 show slight notable changes. For projected scenarios in 2034 and 2050, low–medium-density residential areas are predicted to increase from 11.1 km2 to 12.3 km2 between 2018 and 2050. Similarly, high-density residential areas are predicted to increase from 18.6 km2 to 22.4 km2 between 2018 and 2050. Assessment of the effects of future climate change on potential soil erosion risk for Epworth district were undertaken by applying the representative concentration pathways (RCP4.5 and RCP8.5) climate scenarios, and model ensemble averages from multiple general circulation models (GCMs) were used to derive the rainfall erosivity factor for the RUSLE model. Average soil loss rates for both climate scenarios, RCP4.5 and RCP8.5, were predicted to be high in 2034 due to the large spatial area extent of croplands and disturbed green spaces exposed to soil erosion processes, therefore increasing potential soil erosion risk, with RCP4.5 having more impact than RCP8.5 due to a higher applied rainfall erosivity. For 2050, the predicted wide area average soil loss rates declined for both climate scenarios RCP4.5 and RCP8.5, following the predicted decline in rainfall erosivity and vulnerable areas that are erodible. Overall, high potential soil erosion risk was predicted along the flanks of the drainage network for both RCP4.5 and RCP8.5 climate scenarios in 2050.

Estimating the Soil Erosion Response to Land-Use Change Using GIS-Based RUSLE and Remote Sensing: A Case Study of Heilongjiang Province, China

“…Since the 21st century, research on soil erosion at home and abroad has often integrated multiple methods for more accurate estimation, with outdoor runoff plot monitoring, indoor model estimation, the stylus method, fixed-point observation, laser scanning, and isotope tracing, as well as other methodological techniques being very widely used [8,[15][16][17][18]. Although there are various methods to study soil erosion, these methods are generally costly and time-consuming to conduct on a large scale.…”

“…Soil erosion models typically comprise both experiential and physical models that can accurately assess soil and water erosion. Physical models, e.g., the water erosion prediction project (WEPP) and pan-European soil erosion risk assessment (PESERA), are more complex, the measurements are more difficult to obtain, and they are not suitable for large area soil evaluations [15][16][17]. The flexibility of the empirical model in areas of data scarcity enhances its reception and application.…”

Scenario Simulation of Land Use and Cover under Safeguarding Ecological Security: A Case Study of Chang-Zhu-Tan Metropolitan Area, China

“…With urbanization and industrialization, the demands of human activities on land resources are increasing, especially in developing metropolitan areas [44]. Although metropolitans have achieved remarkable progress in socioeconomic areas, unprecedented transformations in land use and cover have occurred and resulted in resources and environmental problems, such as declining air quality [45], extreme traffic by congestion [46], high potential in soil erosion risk [47], lowered water tables [48], urban waterlogging [49], deforestation [50], and occupied cropland [51]. At the same time, these consequences restricted the transition of socioeconomic areas and reduced the level of regional ecological security [52].…”